35 articles in this category.
Pure model scaling is no longer the whole story. A practical map of where the next serious gains are coming from: inference-time compute, retrieval design, tool integration, and human-in-the-loop systems.
A practical deep dive on open-weight reasoning models in 2026: definitions, architecture patterns, strengths, risks, and how to decide when open weights beat closed APIs.
The practical shift in 2026 is not just better model outputs. It is LLMs acting as controllers over files, tools, and workflows, and that same control pattern now shows up in physical AI and robotics.
A practical 2026 deep dive into how LLMs moved from parameter races to production architecture: RAG-first systems, usable long context, multimodality, agent workflows, and hybrid deployment.
A practical guide to designing open-domain AI systems with one concrete port-compliance case, failure containment patterns, and a production-grade evaluation workflow.
A practical team worksheet for evaluating open-domain AI tasks: evidence quality, uncertainty handling, and recovery behavior under messy real-world conditions.
Discover how Grouped Query Attention became the secret weapon behind 1M+ token context windows in 2025's flagship models, enabling massive scaling without exploding memory costs.
Discover the fascinating process behind how transformers predict text, from tokenization to probability distributions, demystifying the core mechanism that powers modern AI.
Discover how Mixture of Experts became the secret to trillion-parameter models in 2025, enabling massive AI scaling while using only a fraction of the compute through revolutionary sparse activation.
Explore the vast landscape of AI techniques beyond LLMs, from computer vision to reinforcement learning, and discover how these technologies integrate to create powerful intelligent systems.
Explore how large language models create a compelling illusion of thought through pattern matching and statistical prediction, despite lacking true understanding or consciousness.
Discover the fascinating process behind how large language models learn from data, the challenges involved in training them, and why high-quality training data is becoming increasingly scarce.
Explore the challenges of balancing real-time responsiveness and latency in large language models, and discover the techniques used to optimize LLM performance for time-sensitive applications.
Explore why large language models struggle with complex questions, and learn practical strategies to help you achieve better results when asking sophisticated queries.
Discover how large language models track and resolve references in text, a crucial capability that enables more coherent conversations and a deeper understanding of complex documents.
A detailed guide on what tokens are, how they work in LLMs, and why they matter for anyone using AI language models.
Explore the origins and impacts of bias in large language models, and learn about the strategies researchers use to create more fair and inclusive AI systems.
Modern LLMs go far beyond simple next-word prediction. Discover how transformers, multimodal inputs, and in-context learning redefine what AI can understand and generate.
Explore how overfitting affects large language models, why it happens, and the techniques used to prevent models from memorizing rather than generalizing from training data.
Explore the different approaches that define how large language models learn, from supervised learning to reinforcement learning from human feedback (RLHF), and understand how each method shapes AI behavior.
Explore how sparse attention techniques allow large language models to process longer inputs more efficiently by focusing only on the most relevant relationships between tokens.
Explore the fascinating mechanisms that enable large language models to understand and process lengthy documents, from attention mechanisms to chunking strategies.
Unravel the mystery of how language models track and maintain context in conversations. Learn about contextual embeddings, reference resolution, and other techniques that enable coherent and relevant responses.
Compare the advantages and limitations of open-source and proprietary LLMs, examining real-world examples like Llama, Mistral, and GPT-4 to understand which approach best fits different use cases.
Understand the computational challenge that makes large language models struggle with longer inputs, and learn about the innovative solutions being developed to overcome this limitation.
Explore how large language models attempt to reason, the surprising capabilities they've demonstrated, and the fundamental limitations that still separate them from human-like thinking.
Learn why complex, multi-layered questions often confuse even advanced AI models, and discover practical strategies for crafting better prompts that get you the answers you need.
Explore the challenges of working with limited context windows in large language models, and learn effective strategies for optimizing your inputs when facing memory constraints.
Discover how memory-enhanced transformers are revolutionizing AI by giving language models a persistent 'notebook' to retain information over time, enabling more coherent long-form interactions.
Dive into the revolutionary architecture that powers today's large language models, understanding how transformers process information and why they've become the foundation of modern AI.
Explore the fundamental architectural differences between dense models like GPT-4 and experts-based models like Switch Transformer, and learn where each approach excels.
Discover how fine-tuning transforms generic language models into specialized tools for specific domains, and learn the practical approaches to implement this powerful technique.
Explore how multimodal LLMs integrate text, images, audio, and video, revolutionizing AI's ability to understand and interact with different types of data.
Exploring the principles behind AI scaling laws and why the future of AI might not just be about building bigger models, but smarter and more efficient ones.
Dive into how attention mechanisms enable LLMs to focus on relevant information in text. Learn about self-attention, multi-head attention, and how they contribute to the remarkable capabilities of modern language models.